Agentic Design

Patterns
🎭

Multimodal RAG(MMRAG)

Retrieval-augmented generation that handles and integrates text, images, audio, video, and structured data sources

Complexity: highKnowledge Retrieval (RAG)

🎯 30-Second Overview

Pattern: Multi-modal RAG system that retrieves and fuses evidence across text, images, audio, and video using specialized encoders

Why: Enables comprehensive understanding by combining information from multiple modalities that text-only systems miss

Key Insight: Per-modality encoders (CLIP, Whisper, BLIP-2) with learned fusion weights for cross-modal evidence integration

⚡ Quick Implementation

1Multi-Modal Ingest:Process text, images, audio, video with modality-specific preprocessing
2Index & Encode:Create per-modality embeddings using specialized encoders (CLIP, BLIP-2)
3Query & Retrieve:Search across modalities using hybrid retrieval strategies
4Fusion & Rerank:Combine multi-modal results with learned fusion weights
5Generate & Cite:Use vision-language models with cross-modal evidence citations
Example: multi_modal_query → [text_search, image_search, audio_search] → fusion → vlm_generation → response

📋 Do's & Don'ts

✅Use modality-specific encoders (CLIP for vision, Whisper for audio)
✅Implement hybrid retrieval combining lexical and vector search per modality
✅Apply learned fusion weights calibrated for different modality combinations
✅Cache OCR, ASR, and visual features to reduce preprocessing overhead
✅Include temporal alignment for audio/video with precise timestamps
❌Rely solely on text embeddings for visual or audio content
❌Skip quality validation for OCR/ASR outputs that may be noisy
❌Inline raw media files instead of using feature references
❌Mix incomparable similarity scores across modalities without calibration
❌Ignore privacy and compliance requirements for sensitive media content

🚦 When to Use

Use When

  • • Document analysis requiring understanding of both text and visual elements
  • • Media-rich content search across images, videos, and audio
  • • E-commerce and product discovery with visual and textual attributes
  • • Educational content combining slides, audio lectures, and notes
  • • Healthcare applications integrating medical imaging with clinical text

Avoid When

  • • Pure text-based applications where other modalities add no value
  • • Strict latency requirements incompatible with multi-modal processing
  • • Limited computational resources unable to handle vision/audio models
  • • Privacy-sensitive environments restricting image/audio processing
  • • Domains with poor OCR/ASR quality where visual/audio signals are unreliable

📊 Key Metrics

Cross-Modal Retrieval
Recall@k and MRR across different modality combinations
Fusion Effectiveness
Performance improvement from multi-modal vs single-modal retrieval
Generation Faithfulness
Accuracy of vision-language model outputs with multi-modal evidence
Citation Quality
Precision of cross-modal evidence attribution and source linking
Modality Coverage
Balanced utilization of available modalities in retrieval results
Processing Efficiency
Latency and cost per modality including preprocessing overhead

💡 Top Use Cases

Scientific Document Analysis: Research papers with figures, tables, and mathematical equations
E-commerce Search: Product discovery combining visual appearance with textual descriptions
Educational Content: Course materials integrating lecture slides, audio, and supplementary text
Technical Support: Troubleshooting guides with screenshots, videos, and written instructions
Medical Research: Clinical studies combining medical imaging, patient records, and literature

References & Further Reading

Deepen your understanding with these curated resources

Foundational Papers & Multimodal RAG Research

Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation (Zhang et al., 2025)

Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts (Wang et al., 2025)

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)

Multimodal RAG: A Comprehensive Survey (Li et al., 2024)

Vision-Language Models & Encoders

CLIP: Learning Transferable Visual Representations From Natural Language Supervision (Radford et al., 2021)

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders (Li et al., 2023)

LLaVA: Large Language and Vision Assistant (Liu et al., 2024)

Chameleon: Mixed-Modal Early-Fusion Foundation Models (Team et al., 2024)

Audio Processing & Speech Recognition

Whisper: Robust Speech Recognition via Large-Scale Weak Supervision (Radford et al., 2022)

Speech-T5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (Ao et al., 2022)

WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing (Chen et al., 2022)

ImageBind: One Embedding Space To Bind Them All (Girdhar et al., 2023)

Multi-Modal Fusion & Architecture

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations (Lu et al., 2019)

LXMERT: Learning Cross-Modality Encoder Representations from Transformers (Tan & Bansal, 2019)

Flamingo: a Visual Language Model for Few-Shot Learning (Alayrac et al., 2022)

ALIGN: Scaling Up Visual and Vision-Language Representation Learning (Jia et al., 2021)

Implementation Frameworks & APIs

OpenAI GPT-4V Vision API Documentation and Best Practices

Anthropic Claude 3 Vision Capabilities and Integration Guide

Google Gemini Multimodal API Documentation

LangChain Multimodal RAG Tutorial and Implementation Examples

Evaluation & Benchmarking

MM-RAG: A Comprehensive Benchmark for Multi-Modal RAG Systems (Chen et al., 2024)

MIRAG: Multimodal Retrieval-Augmented Generation Benchmark (Zhang et al., 2024)

VQA: Visual Question Answering Evaluation Framework

TextVQA: Questions Requiring Reading and Reasoning about Text in Images

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